Introduction

• Who am I? Professor of Transport Data Science at the University of Leeds.
• My focus: Building open, reproducible, and policy-relevant transport planning tools.
• Today’s topic: Building capacity for data-driven decision-making in urban mobility.
• Why it matters for Southeast Europe: Supporting sustainable mobility goals and the path to EU collaboration.

The Challenge: The Data-to-Policy Gap

We have more data than ever before, but are we using it effectively?

Common Barriers:

• Data Access: Data is often siloed, proprietary, or not in a usable format.
• Skills & Tools: Lack of training and accessible tools to analyse the data.
• Institutional Culture: Resistance to new methods and a disconnect between analysts and decision-makers.
• Procurement: Over-reliance on ‘black box’ commercial solutions.

A Solution: Open & Reproducible Workflows

Openness builds trust and empowers collaboration.

Example community of practice: QGIS

Why is QGIS so strong?

Aside: Geographic distribution of QGIS interest

Pillar 1: Open Data & Standards

Good analysis starts with good data.

• Data Standards are Key:
  • GTFS for public transport schedules.
  • GBFS for shared micro-mobility (bikes, scooters).
  • OpenStreetMap for detailed street network data.
• National Data Portals: A vital resource for official statistics and geographic data.
• Example: Sourcing road network data from OpenStreetMap for a whole country.

Pillar 2: Open Source Tools

Powerful, free, and adaptable tools for transport analysis.

• The R & Python Ecosystems: Mature, extensive libraries for data science, statistics, and visualisation.
• Example (R): The stplanr package for transport planning and modelling.
• Example (Python): geopandas and pandana for network analysis.
• These tools can be adapted to local needs and data sources.

Pillar 3: Communities of Practice

Capacity building is not just about tools, it’s about people.

• A ‘Community of Practice’ is a group of people who “share a concern or a passion for something they do and learn how to do it better as they interact regularly” (Wenger 1998).
• Open source projects naturally foster these communities.
• Examples: rOpenSci, rOpenSpain, QGIS user groups.
• They provide support, share best practices, and drive innovation.
• Key takeaway: To build capacity, invest in building communities.

Case Study 1: National-Scale Planning (UK)

From local data to national strategy with plan.activetravelengland.gov.uk.

• Purpose: Prioritise billions of £ in investment for walking and cycling.
• How it works:
  • Integrates dozens of open datasets (e.g., census, road safety, network data).
  • Uses a transparent, open-source model (pct R package).
  • Provides a web interface for planners across the country.
• Impact: A consistent, evidence-based approach to national transport planning.

Active Travel England Tool: plan.activetravelengland.gov.uk

Case Study 2: Regional Planning (Scotland)

The Network Planning Tool (npt.scot).

• Purpose: Help regional and local authorities design and prioritise active travel networks.
• Features:
  • Open data and transparent methods.
  • Estimates of cycling potential and Level of Service.
  • Web UI for planners and the public.
• Lesson: A powerful tool can be developed and deployed for a specific region, and can serve as a model for others.

Network Planning Tool for Scotland: npt.scot

Case Study 3: National OD data in Spain

• The spanishoddata package provides access to Spain’s national origin-destination mobility survey.
• It’s a community-driven project, adopted by rOpenSpain, ensuring long-term maintenance and quality.
• The tool is used by researchers and public bodies, including the Spanish Ministry of Transport, for evidence-based planning.
• This demonstrates a successful model of collaboration between government, academia, and the open-source community.

Mobility analysis with spanishoddata

Making it Happen in Southeast Europe

How can you apply these principles?

1. Start with a Data Audit: What data do you have? What format is it in? What can be opened?
2. Run a Pilot Project: Choose a specific problem (e.g., mapping cycling potential in one city) and solve it with open tools.
3. Invest in Training: Build skills in R or Python for spatial data analysis within your statistical offices and transport authorities.
4. Foster Regional Collaboration: Share data, code, and experiences with neighbouring countries facing similar challenges.

Future Directions: New Data & New Challenges

The landscape is always changing.

• New Data Sources: Mobile phone data, GPS tracks, sensor data offer huge potential.
  • Companies can be paid or mandated to share anonymised data for public good, e.g. mobile network operators.
• New Challenges:
  • Quality & Bias: Are these new datasets representative?
  • Privacy & Ethics: How do we use sensitive data responsibly?
  • Governance: Who owns the data and the insights?
• New tools provide new ways to add value to existing datasets

Jittering Method for Privacy Preservation

Source: Kotov, Lovelace, and Vidal-Tortosa (2024)

Demo: Mapping Southeast Europe with Open Tools

Results created with the eurostat and giscoR packages to download and map official geospatial data for the region.

Southeast Europe Map